OpenRaiser/Intern-Atlas huggingface dataset:
Neural Network Approaches for Data Estimation in Unique Word OFDM Systems
Venue: arXiv cs.IT 2022
Year: 2022

Data estimation is conducted with model-based estimation methods since the beginning of digital communications. However, motivated by the growing success of machine learning, current research focuses on replacing model-based data estimation methods by data-driven approaches, mainly neural networks (NNs). In this work, we particularly investigate the incorporation of existing model knowledge into data-driven approaches, which is expected to lead to complexity reduction and / or performance enhancement. We describe three different options, namely "model-inspired'' pre-processing, choosing an NN architecture motivated by the properties of the underlying communication system, and inferring the layer structure of an NN with the help of model knowledge. Most of the current publications on NN-based data estimation deal with general multiple-input multiple-output communication (MIMO) systems. In this work, we investigate NN-based data estimation for so-called unique word orthogonal frequency division multiplexing (UW-OFDM) systems. We highlight differences between UW-OFDM systems and general MIMO systems one has to be aware of when using NNs for data estimation, and we introduce measures for successful utilization of NN-based data estimators in UW-OFDM systems. Further, we investigate the use of NNs for data estimation when channel coded data transmission is conducted, and we present adaptions to be made, such that NN-based data estimators provide satisfying performance for this case. We compare the presented NNs concerning achieved bit error ratio performance and computational complexity, we show the peculiar distributions of their data estimates, and we also point out their downsides compared to model-based equalizers.
Towards automating Numerical Consistency Checks in Financial Reports
Venue: arXiv cs.CL 2022
Year: 2022

We introduce KPI-Check, a novel system that automatically identifies and cross-checks semantically equivalent key performance indicators (KPIs), e.g. "revenue" or "total costs", in real-world German financial reports. It combines a financial named entity and relation extraction module with a BERT-based filtering and text pair classification component to extract KPIs from unstructured sentences before linking them to synonymous occurrences in the balance sheet and profit & loss statement. The tool achieves a high matching performance of $73.00$% micro F$_1$ on a hold out test set and is currently being deployed for a globally operating major auditing firm to assist the auditing procedure of financial statements.
A parameterized halting problem, $Δ_0$ truth and the MRDP theorem
Venue: arXiv cs.CC 2022
Year: 2022

We study the parameterized complexity of the problem to decide whether a given natural number $n$ satisfies a given $Δ_0$-formula $φ(x)$; the parameter is the size of $φ$. This parameterization focusses attention on instances where $n$ is large compared to the size of $φ$. We show unconditionally that this problem does not belong to the parameterized analogue of $\mathsf{AC}^0$. From this we derive that certain natural upper bounds on the complexity of our parameterized problem imply certain separations of classical complexity classes. This connection is obtained via an analysis of a parameterized halting problem. Some of these upper bounds follow assuming that $IΔ_0$ proves the MRDP theorem in a certain weak sense.
Chopin: Combining Distributed and Centralized Schedulers for Self-Adjusting Datacenter Networks
Venue: arXiv cs.DC 2022
Year: 2022

The performance of distributed and data-centric applications often critically depends on the interconnecting network. Emerging reconfigurable datacenter networks (RDCNs) are a particularly innovative approach to improve datacenter throughput. Relying on a dynamic optical topology which can be adjusted towards the workload in a demand-aware manner, RDCNs allow to exploit temporal and spatial locality in the communication pattern, and to provide topological shortcuts for frequently communicating racks. The key challenge, however, concerns how to realize demand-awareness in RDCNs in a scalable fashion. This paper presents and evaluates Chopin, a hybrid scheduler for self-adjusting networks that provides demand-awareness at low overhead, by combining centralized and distributed approaches. Chopin allocates optical circuits to elephant flows, through its slower centralized scheduler, utilizing global information. Chopin's distributed scheduler is orders of magnitude faster and can swiftly react to changes in the traffic and adjust the optical circuits accordingly, by using only local information and running at each rack separately.
Coarse-graining of CFD-DEM for simulation of sand production in the modified cohesive contact model
Venue: arXiv cs.CE 2022
Year: 2022

Sand production is an important issue for many hydrocarbon recovery applications in unconsolidated reservoirs. The model using the Computational Fluid Dynamics coupled with Discrete Element Method (CFD-DEM) can capture micro-scale features of sand transport problems. In this study, a coarse-graining approach of 3D CFD-DEM model is developed for the sand production phenomenon using the sample based on the particle size distribution (PSD) from the Kazakhstan reservoir field. The derivation of scaling from a fine to a coarse model is presented rigorously. The original (fine scale) model is validated to the laboratory results including the sand production rate. The results of the original model is compared to the SSW and SSP coarse-gained models. The SSW model results agrees with the result of the sand production rate for the original system. We also observe a good match of the fluid velocity streamline and the produced particle distribution between the original and the coarse models. The speedup of the coarse model is up to 9.4 in the parallelized coarse-grained model.
The Simplest Balance Controller for Dynamic Walking
Venue: arXiv cs.RO 2022
Year: 2022

Humans can balance very well during walking, even when perturbed. But it seems difficult to achieve robust walking for bipedal robots. Here we describe the simplest balance controller that leads to robust walking for a linear inverted pendulum (LIP) model. The main idea is to use a linear function of the body velocity to determine the next foot placement, which we call linear foot placement control (LFPC). By using the Poincaré map, a balance criterion is derived, which shows that LFPC is stable when the velocity-feedback coefficient is located in a certain range. And that range is much bigger when stepping faster, which indicates "faster stepping, easier to balance". We show that various gaits can be generated by adjusting the controller parameters in LFPC. Particularly, a dead-beat controller is discovered that can lead to steady-state walking in just one step. The effectiveness of LFPC is verified through Matlab simulation as well as V-REP simulation for both 2D and 3D walking. The main feature of LFPC is its simplicity and inherent robustness, which may help us understand the essence of how to maintain balance in dynamic walking.
A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs
Venue: arXiv cs.LG 2022
Year: 2022

We introduce a new benchmark dataset, Placenta, for node classification in an underexplored domain: predicting microanatomical tissue structures from cell graphs in placenta histology whole slide images. This problem is uniquely challenging for graph learning for a few reasons. Cell graphs are large (>1 million nodes per image), node features are varied (64-dimensions of 11 types of cells), class labels are imbalanced (9 classes ranging from 0.21% of the data to 40.0%), and cellular communities cluster into heterogeneously distributed tissues of widely varying sizes (from 11 nodes to 44,671 nodes for a single structure). Here, we release a dataset consisting of two cell graphs from two placenta histology images totalling 2,395,747 nodes, 799,745 of which have ground truth labels. We present inductive benchmark results for 7 scalable models and show how the unique qualities of cell graphs can help drive the development of novel graph neural network architectures.
BioJam Camp: toward justice through bioengineering and biodesign co-learning with youth
Venue: arXiv cs.CY 2022
Year: 2022

BioJam is a political, artistic, and educational project in which Bay Area artists, scientists, and educators collaborate with youth and communities of color to address historical exclusion of their communities in STEM fields and reframe what science can be. As an intergenerational collective, we co-learn on topics of culture (social and biological), community (cultural and ecological), and creativity. We reject the notion that increasing the number of scientists of color requires inculcation in the ways of the dominant culture. Instead, we center cultural practices, traditional ways of knowing, storytelling, art, experiential learning, and community engagement to break down the framing that positions these practices as distinct from science. The goal of this work is to realize a future in which the practice of science is relatable, accessible, and liberatory.
Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence
Venue: arXiv cs.CY 2022
Year: 2022

In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Peter Stone of the University of Texas at Austin. The report, entitled "Artificial Intelligence and Life in 2030," examines eight domains of typical urban settings on which AI is likely to have impact over the coming years: transportation, home and service robots, healthcare, education, public safety and security, low-resource communities, employment and workplace, and entertainment. It aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential and to help guide decisions in industry and governments, as well as to inform research and development in the field. The charge for this report was given to the panel by the AI100 Standing Committee, chaired by Barbara Grosz of Harvard University.
Secure Aggregation Is Not All You Need: Mitigating Privacy Attacks with Noise Tolerance in Federated Learning
Venue: arXiv cs.CR 2022
Year: 2022

Federated learning is a collaborative method that aims to preserve data privacy while creating AI models. Current approaches to federated learning tend to rely heavily on secure aggregation protocols to preserve data privacy. However, to some degree, such protocols assume that the entity orchestrating the federated learning process (i.e., the server) is not fully malicious or dishonest. We investigate vulnerabilities to secure aggregation that could arise if the server is fully malicious and attempts to obtain access to private, potentially sensitive data. Furthermore, we provide a method to further defend against such a malicious server, and demonstrate effectiveness against known attacks that reconstruct data in a federated learning setting.
Excel Spreadsheet Analyzer
Venue: arXiv cs.SE 2022
Year: 2022

Spreadsheets are widely used in various fields to do large numerical analysis. While several companies have relied on spreadsheets for decades, data scientists are going in the direction of using scientific programming languages such as python to do their data analysis due to the support, community, and vast amount of libraries. While using python to analyze a company's spreadsheets, some information such as the formulas and dependencies of a cell are lost. We propose a tool that creates an abstract intermediate representation (AIR) of a spreadsheet. This representation facilitates the transfer from spreadsheets into scientific programming languages while preserving inter-dependency information about data. In addition to that, we build a python library on top of our tool to perform some data analysis in python.
A unified platform for GIS data management and analytics
Venue: arXiv cs.CY 2022
Year: 2022

In this work we propose a software platform for the collection, visualization, management and analysis of heterogeneous and multisource data for soil characteristics estimation. The platform is designed in such a way that it can easily handle proximity, airborne and spaceborne data and provides to the final user all the tools and proper visualizations to perform precision agriculture. The proposed software allows an easy integration of new data (that can be performed directly on-board of the acquisition device) and the possibility to add custom predictive systems for soil characteristic estimation. Usability experiments show that the platform is easy to use and effective.
Emergency action termination for immediate reaction in hierarchical reinforcement learning
Venue: arXiv cs.LG 2022
Year: 2022

Hierarchical decomposition of control is unavoidable in large dynamical systems. In reinforcement learning (RL), it is usually solved with subgoals defined at higher policy levels and achieved at lower policy levels. Reaching these goals can take a substantial amount of time, during which it is not verified whether they are still worth pursuing. However, due to the randomness of the environment, these goals may become obsolete. In this paper, we address this gap in the state-of-the-art approaches and propose a method in which the validity of higher-level actions (thus lower-level goals) is constantly verified at the higher level. If the actions, i.e. lower level goals, become inadequate, they are replaced by more appropriate ones. This way we combine the advantages of hierarchical RL, which is fast training, and flat RL, which is immediate reactivity. We study our approach experimentally on seven benchmark environments.
Õptimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization
Venue: arXiv cs.LG 2022
Year: 2022

The problem of learning threshold functions is a fundamental one in machine learning. Classical learning theory implies sample complexity of $O(ξ^{-1} \log(1/β))$ (for generalization error $ξ$ with confidence $1-β$). The private version of the problem, however, is more challenging and in particular, the sample complexity must depend on the size $|X|$ of the domain. Progress on quantifying this dependence, via lower and upper bounds, was made in a line of works over the past decade. In this paper, we finally close the gap for approximate-DP and provide a nearly tight upper bound of $\tilde{O}(\log^* |X|)$, which matches a lower bound by Alon et al (that applies even with improper learning) and improves over a prior upper bound of $\tilde{O}((\log^* |X|)^{1.5})$ by Kaplan et al. We also provide matching upper and lower bounds of $\tildeΘ(2^{\log^*|X|})$ for the additive error of private quasi-concave optimization (a related and more general problem). Our improvement is achieved via the novel Reorder-Slice-Compute paradigm for private data analysis which we believe will have further applications.
An investigation of security controls and MITRE ATT\&CK techniques
Venue: arXiv cs.CR 2022
Year: 2022

Attackers utilize a plethora of adversarial techniques in cyberattacks to compromise the confidentiality, integrity, and availability of the target organizations and systems. Information security standards such as NIST, ISO/IEC specify hundreds of security controls that organizations can enforce to protect and defend the information systems from adversarial techniques. However, implementing all the available controls at the same time can be infeasible and security controls need to be investigated in terms of their mitigation ability over adversarial techniques used in cyberattacks as well. The goal of this research is to aid organizations in making informed choices on security controls to defend against cyberthreats through an investigation of adversarial techniques used in current cyberattacks. In this study, we investigated the extent of mitigation of 298 NIST SP800-53 controls over 188 adversarial techniques used in 669 cybercrime groups and malware cataloged in the MITRE ATT\&CK framework based upon an existing mapping between the controls and techniques. We identify that, based on the mapping, only 101 out of 298 control are capable of mitigating adversarial techniques. However, we also identify that 53 adversarial techniques cannot be mitigated by any existing controls, and these techniques primarily aid adversaries in bypassing system defense and discovering targeted system information. We identify a set of 20 critical controls that can mitigate 134 adversarial techniques, and on average, can mitigate 72\% of all techniques used by 98\% of the cataloged adversaries in MITRE ATT\&CK. We urge organizations, that do not have any controls enforced in place, to implement the top controls identified in the study.
Computationally Light Spectrally Normalized Memory Neuron Network based Estimator for GPS-Denied operation of Micro UAV
Venue: arXiv cs.RO 2022
Year: 2022

This paper addresses the problem of position estimation in UAVs operating in a cluttered environment where GPS information is unavailable. A model learning-based approach is proposed that takes in the rotor RPMs and past state as input and predicts the one-step-ahead position of the UAV using a novel spectral-normalized memory neural network (SN-MNN). The spectral normalization guarantees stable and reliable prediction performance. The predicted position is transformed to global coordinate frame which is then fused along with the odometry of other peripheral sensors like IMU, barometer, compass etc., using the onboard extended Kalman filter to estimate the states of the UAV. The experimental flight data collected from a motion capture facility using a micro-UAV is used to train the SN-MNN. The PX4-ECL library is used to replay the flight data using the proposed algorithm, and the estimated position is compared with actual ground truth data. The proposed algorithm doesn't require any additional onboard sensors, and is computationally light. The performance of the proposed approach is compared with the current state-of-art GPS-denied algorithms, and it can be seen that the proposed algorithm has the least RMSE for position estimates.
Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement
Venue: arXiv cs.SD 2022
Year: 2022

Self-supervised speech representation learning aims to extract meaningful factors from the speech signal that can later be used across different downstream tasks, such as speech and/or emotion recognition. Existing models, such as HuBERT, however, can be fairly large thus may not be suitable for edge speech applications. Moreover, realistic applications typically involve speech corrupted by noise and room reverberation, hence models need to provide representations that are robust to such environmental factors. In this study, we build on the so-called DistilHuBERT model, which distils HuBERT to a fraction of its original size, with three modifications, namely: (i) augment the training data with noise and reverberation, while the student model needs to distill the clean representations from the teacher model; (ii) introduce a curriculum learning approach where increasing levels of noise are introduced as the model trains, thus helping with convergence and with the creation of more robust representations; and (iii) introduce a multi-task learning approach where the model also reconstructs the clean waveform jointly with the distillation task, thus also acting as an enhancement step to ensure additional environment robustness to the representation. Experiments on three SUPERB tasks show the advantages of the proposed method not only relative to the original DistilHuBERT, but also to the original HuBERT, thus showing the advantages of the proposed method for ``in the wild'' edge speech applications.
Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling
Venue: arXiv cs.SI 2022
Year: 2022

Temporal networks are suitable for modeling complex evolving systems. It has a wide range of applications, such as social network analysis, recommender systems, and epidemiology. Recently, modeling such dynamic systems has drawn great attention in many domains. However, most existing approaches resort to taking discrete snapshots of the temporal networks and modeling all events with equal importance. This paper proposes Significant Ties Graph Neural Networks (STGNN), a novel framework that captures and describes significant ties. To better model the diversity of interactions, STGNN introduces a novel aggregation mechanism to organize the most significant historical neighbors' information and adaptively obtain the significance of node pairs. Experimental results on four real networks demonstrate the effectiveness of the proposed framework.
ABCAS: Adaptive Bound Control of spectral norm as Automatic Stabilizer
Venue: arXiv cs.CV 2022
Year: 2022

Spectral Normalization is one of the best methods for stabilizing the training of Generative Adversarial Network. Spectral Normalization limits the gradient of discriminator between the distribution between real data and fake data. However, even with this normalization, GAN's training sometimes fails. In this paper, we reveal that more severe restriction is sometimes needed depending on the training dataset, then we propose a novel stabilizer which offers an adaptive normalization method, called ABCAS. Our method decides discriminator's Lipschitz constant adaptively, by checking the distance of distributions of real and fake data. Our method improves the stability of the training of Generative Adversarial Network and achieved better Fréchet Inception Distance score of generated images. We also investigated suitable spectral norm for three datasets. We show the result as an ablation study.
Mining Mathematical Documents for Question Answering via Unsupervised Formula Labeling
Venue: arXiv cs.IR 2022
Year: 2022

The increasing number of questions on Question Answering (QA) platforms like Math Stack Exchange (MSE) signifies a growing information need to answer math-related questions. However, there is currently very little research on approaches for an open data QA system that retrieves mathematical formulae using their concept names or querying formula identifier relationships from knowledge graphs. In this paper, we aim to bridge the gap by presenting data mining methods and benchmark results to employ Mathematical Entity Linking (MathEL) and Unsupervised Formula Labeling (UFL) for semantic formula search and mathematical question answering (MathQA) on the arXiv preprint repository, Wikipedia, and Wikidata, which is part of the Wikimedia ecosystem of free knowledge. Based on different types of information needs, we evaluate our system in 15 information need modes, assessing over 7,000 query results. Furthermore, we compare its performance to a commercial knowledge-base and calculation-engine (Wolfram Alpha) and search-engine (Google). The open source system is hosted by Wikimedia at https://mathqa.wmflabs.org. A demovideo is available at purl.org/mathqa.
Open Higher-Order Logic (Long Version)
Venue: arXiv cs.LO 2022
Year: 2022

We introduce a variation on Barthe et al.'s higher-order logic in which formulas are interpreted as predicates over open rather than closed objects. This way, concepts which have an intrinsically functional nature, like continuity, differentiability, or monotonicity, can be expressed and reasoned about in a very natural way, following the structure of the underlying program. We give open higher-order logic in distinct flavors, and in particular in its relational and local versions, the latter being tailored for situations in which properties hold only in part of the underlying function's domain of definition.